Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma.

Jingwei Zhao,Le Wang,Bo Wei

doi:10.1155/2020/3708231

Abstract

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR = 3.192, 95%CI = 2.182‐4.670; CGGA: HR = 1.922, 95%CI = 1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

Highlights

Lower-grade gliomas (LGG) that include World Health Organization (WHO) grade II and III diffuse gliomas are common infiltrative brain tumors in adults [1]
Cheng et al identified that highly expressed long noncoding RNAs (lncRNAs) X-inactive specific transcript (XIST) may promote cell viability, migration, invasion, and resistance to apoptosis by increasing glucose uptake, with the mechanism referring to upregulation of glucose transporters GLUT1 and GLUT3 [12]. The study of He et al revealed that upregulated lncRNA UCA1 may induce glycolysis and invasion in glioma cells by competitively binding to miR-182 and influencing the downstream target of miR-182 [13]. These findings indicated that the metabolism-related lncRNAs may have underlying prognostic values for LGG; no studies focused on the lncRNA signature until now
Based on the Linear Models for Microarray Data (LIMMA) method, 1613 mRNAs and 37 lncRNAs were found to be differentially expressed between LGG and normal brain tissues in the GSE4290 dataset (Figure 1(a))

Summary

Introduction

Lower-grade gliomas (LGG) that include World Health Organization (WHO) grade II and III diffuse gliomas are common infiltrative brain tumors in adults [1]. Qi et al extracted the fatty acid catabolic metabolism-related genes from Molecular Signatures Database (MsigDB) and identified an 8-gene risk signature using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis based on RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) dataset and The Cancer Genome Atlas (TCGA) dataset. This risk signature was found to be an independent prognostic factor for patients with all grade gliomas (CGGA: hazard ratios ðHRÞ = 4:0044, 95%confidence intervals ðCIÞ = 2:7634‐5:8028; TCGA: HR = 1:7382, 95%CI = 1:0577‐ 2:8567) [6]. Energy metabolism-related prognostic biomarkers for LGG remain rarely reported

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